Ageing, an unavoidable aspect of life, involves a decline in the body's ability to meet physiological demands. Biological age (BA) is a key biomarker that differs from chronological age and can provide insights into an individual's health status. Accurately estimating BA and understanding its relationship with chronic diseases is crucial for early intervention and better health management.
Researchers have developed an innovative multimodal image Transformer system. This system, for the first time, combines facial, tongue, and retinal images to estimate biological age. The study involved 11,223 healthy subjects and 2,840 individuals with chronic diseases, utilising a vast array of images for comprehensive analysis.
Results from the study
Accuracy in Biological Age Estimation: The study demonstrated that the multimodal image Transformer system, which integrates facial, tongue, and retinal images, provided a more accurate estimation of biological age compared to methods using single modalities. This advancement is significant as it offers a more comprehensive view of an individual's biological ageing process.
Biological and Chronological Age Discrepancy: A key discovery was the variation between biological age and chronological age (referred to as AgeDiff). This discrepancy was more pronounced in individuals with chronic diseases, suggesting that biological age is a more reliable indicator of health status than chronological age.
AgeDiff as a Biomarker for Chronic Diseases: The study identified AgeDiff as a potential standalone biomarker for disease risk stratification and progression prediction. Individuals with a higher AgeDiff were found to be at an increased risk of chronic diseases such as heart disease, diabetes, and hypertension. This finding is pivotal as it provides a new metric for early detection and management of these conditions.
Risk Stratification and Disease Prediction: The research demonstrated the utility of categorising AgeDiff for stratifying the risk of developing chronic diseases. This approach allows for a more personalised assessment of health risks, aiding in early intervention and better health management strategies.
Comparison with Traditional Methods: The multimodal approach used in the study was shown to outperform traditional methods in predicting health risks. This indicates a significant advancement in the field of biophysics and computational biology, offering a more robust and accurate tool for managing ageing-related diseases.
Implications for Disease Prediction and Health Management
The implications of the research span across various aspects of medical science and healthcare:
Personalised Medicine and Early Intervention: The study's approach to estimating biological age using multimodal images introduces a new dimension in personalised medicine. It allows for the early detection of age-related diseases, enabling timely intervention and potentially altering the course of these conditions.
Enhanced Risk Assessment for Chronic Diseases: The ability to accurately estimate biological age and its deviation from chronological age provides a powerful tool for assessing the risk of chronic diseases. This could lead to more effective screening programs and targeted prevention strategies.
Revolutionising Geriatric Care: For the ageing population, this research offers a pathway to more nuanced and effective geriatric care. By understanding the biological age, healthcare providers can better tailor treatments and lifestyle recommendations to the actual physiological state of the elderly.
Advancing Health Monitoring Technologies: The findings could stimulate the development of advanced health monitoring technologies that use multimodal image analysis. This would make regular health assessments more accessible and less invasive, promoting proactive health management.
Improving Disease Progression Models: With AgeDiff as a potential biomarker, researchers and clinicians can improve models for predicting the progression of chronic diseases. This would aid in developing more effective treatment protocols and potentially slowing disease progression.
Transforming Health Insurance and Policy: The research could influence health insurance models and public health policies by providing a more accurate assessment of an individual's health risk. Insurance premiums and health resources allocation could become more reflective of an individual's biological age rather than chronological age.
Educating Public on Ageing and Health: This research can also play a vital role in educating the public about the importance of biological age and its impact on health. It can lead to increased awareness about lifestyle choices that affect biological ageing, encouraging healthier habits.
Potential for Broader Applications: Beyond its immediate implications in healthcare, this research has the potential for broader applications in fields like sports science for optimising training regimens, in the workplace for better understanding the ageing workforce, and even in the cosmetic industry for personalised skincare.
Credits and Acknowledgements
This work was conducted at Peking University and published in PNAS. The study marks a significant advancement in biophysics and computational biology.
